Download models in parallel

README.md

@@ -15,6 +15,7 @@ See the Speedometer repo for a more detailed explanation, e.g., in which phases
     - Workloads are in `resources/transformers-js/` and `resources/litert-js`.
     - Shared files are in `resources/shared/`, which is depended-upon as a local package.
     - The default suite / tests to run are in `resources/default-tests.mjs`.
+- Cleaning build artifacts and cached models (optional): Run `npm run clean` in the root directory. This will delete the `dist` directories and clear cached models for all workloads (while preserving the large `gemma` model to avoid unnecessary re-downloads).
 
 ## How to Run Individual Workload
 

package.json

@@ -16,6 +16,7 @@
     "license": "SEE LICENSE IN LICENSE",
     "scripts": {
         "build": "node script/build.mjs",
+        "clean": "node script/clean.mjs",
         "dev": "node tests/server.mjs",
         "lint:check": "eslint **/*.{js,mjs,jsx,ts,tsx}",
         "lint:fix": "eslint \"**/*.{js,mjs,jsx,ts,tsx}\" --fix",

resources/experimental/src/download-models.mjs

@@ -32,29 +32,11 @@ async function downloadModels() {
     env.allowRemoteModels = true; 
 
     try {
-        // Download models that work with pipeline
-        for (const modelInfo of MODELS_TO_DOWNLOAD) {
-            const { id: modelId, task: modelTask, dtype: modelDType } = modelInfo;
-
-            const cacheKey = `${modelId}-${modelTask}-${modelDType}`;
-            if (cache.has(cacheKey)) {
-                console.log(`Model ${modelId} (${modelTask}, dtype: ${modelDType}) already cached. Skipping.`);
-                continue;
-            }
-
-            console.log(`Downloading files for ${modelId} (${modelTask}, dtype: ${modelDType})...`);
-
-            await retry(() => pipeline(
-                modelTask, 
-                modelId, 
-                { 
-                    cache_dir: env.localModelPath,
-                    dtype: modelDType
-                }));
-
-            console.log(`Successfully downloaded and cached ${modelId}`);
-            cache.put(cacheKey);
-        }
+        console.log(`Downloading all experimental models in parallel...`);
+        await Promise.all(
+            MODELS_TO_DOWNLOAD.map(modelInfo => downloadPipelineModel(modelInfo, cache))
+        );
+        console.log(`Successfully checked and downloaded all models.`);
 
     } catch (err) {
         console.error("Model download failed:", err);
@@ -64,6 +46,29 @@ async function downloadModels() {
     env.allowRemoteModels = originalAllowRemote;
 }
 
+async function downloadPipelineModel(modelInfo, cache) {
+    const { id: modelId, task: modelTask, dtype: modelDType } = modelInfo;
+
+    const cacheKey = `${modelId}-${modelTask}-${modelDType}`;
+    if (cache.has(cacheKey)) {
+        console.log(`Model ${modelId} (${modelTask}, dtype: ${modelDType}) already cached. Skipping.`);
+        return;
+    }
+
+    console.log(`Downloading files for ${modelId} (${modelTask}, dtype: ${modelDType})...`);
+
+    await retry(() => pipeline(
+        modelTask, 
+        modelId, 
+        { 
+            cache_dir: env.localModelPath,
+            dtype: modelDType
+        }));
+
+    console.log(`Successfully downloaded and cached ${modelId}`);
+    cache.put(cacheKey);
+}
+
 downloadModels().catch(err => {
     console.error("Download process terminated.");
     process.exit(1);

resources/litert-js/src/download-models.mjs

@@ -11,17 +11,17 @@ const CACHE_VERSION = 1;
 
 const MODELS_TO_DOWNLOAD = [
     {
-        repo: 'qualcomm/MediaPipe-Selfie-Segmentation', 
+        repo: 'qualcomm/MediaPipe-Selfie-Segmentation',
         filename: 'mediapipe_selfie-tflite-float.zip',
         url: 'https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mediapipe_selfie/releases/v0.46.0/mediapipe_selfie-tflite-float.zip'
     },
-    { 
-        repo: 'qualcomm/MobileNet-v3-Small', 
+    {
+        repo: 'qualcomm/MobileNet-v3-Small',
         filename: 'mobilenet_v3_small-tflite-float.zip',
         url: 'https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mobilenet_v3_small/releases/v0.46.0/mobilenet_v3_small-tflite-float.zip'
     },
-    { 
-        repo: 'qualcomm/MediaPipe-Hand-Detection', 
+    {
+        repo: 'qualcomm/MediaPipe-Hand-Detection',
         filename: 'mediapipe_hand-tflite-float.zip',
         url: 'https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/mediapipe_hand/releases/v0.46.0/mediapipe_hand-tflite-float.zip'
     }
@@ -34,59 +34,68 @@ async function downloadModels() {
 
     if (!fs.existsSync(MODEL_DIR)) {
         console.log(`Creating directory: **${MODEL_DIR}**`);
-        fs.mkdirSync(MODEL_DIR, { recursive: true }); 
+        fs.mkdirSync(MODEL_DIR, { recursive: true });
     }
 
     console.log(`Starting TFLite model downloads to: **${MODEL_DIR}**`);
 
-    for (const modelInfo of MODELS_TO_DOWNLOAD) {
-        const { repo, filename, url } = modelInfo;
+    try {
+        await Promise.all(
+            MODELS_TO_DOWNLOAD.map(modelInfo => downloadModel(modelInfo, cache))
+        );
+    } catch (err) {
+        console.error("TFLite model download failed:", err);
+        throw err;
+    }
+    console.log('TFLite download process finished.');
+}
 
-        const cacheKey = `${repo}-${filename}`;
-        if (cache.has(cacheKey)) {
-            console.log(`Model ${filename} from ${repo} already cached. Skipping.`);
-            continue;
-        }
+async function downloadModel(modelInfo, cache) {
+    const { repo, filename, url } = modelInfo;
 
-        const modelUrl = url;
-        const outputPath = path.join(MODEL_DIR, path.basename(filename));
+    const cacheKey = `${repo}-${filename}`;
+    if (cache.has(cacheKey)) {
+        console.log(`Model ${filename} from ${repo} already cached. Skipping.`);
+        return;
+    }
 
-        console.log(`\nAttempting to download **${filename}** from **${repo}**...`);
-        console.log(`URL: ${modelUrl}`);
+    const modelUrl = url;
+    const outputPath = path.join(MODEL_DIR, path.basename(filename));
 
-        try {
-            await retry(async () => {
-                const response = await fetch(modelUrl);
+    console.log(`\nAttempting to download **${filename}** from **${repo}**...`);
+    console.log(`URL: ${modelUrl}`);
 
-                if (!response.ok) {
-                    throw new Error(`Failed to fetch: ${response.statusText} (${response.status})`);
-                }
+    try {
+        await retry(async () => {
+            const response = await fetch(modelUrl);
 
-                const fileStream = fs.createWriteStream(outputPath);
-                await new Promise((resolve, reject) => {
-                    response.body.pipe(fileStream);
-                    response.body.on('error', reject);
-                    fileStream.on('finish', resolve);
-                });
-            });
-
-            console.log(`Successfully downloaded **${filename}** to **${outputPath}**`);
-
-            if (path.extname(filename) === '.zip') {
-                console.log(`Extracting **${filename}**...`);
-                const zip = new AdmZip(outputPath);
-                zip.extractAllTo(MODEL_DIR, true);
-                console.log(`Successfully extracted **${filename}** to **${MODEL_DIR}**`);
-                fs.unlinkSync(outputPath);
-                console.log(`Deleted zip file **${outputPath}**`);
+            if (!response.ok) {
+                throw new Error(`Failed to fetch: ${response.statusText} (${response.status})`);
             }
 
-            cache.put(cacheKey);
-        } catch (err) {
-            console.error(`Model download failed for ${repo}/${filename} after retries:`, err.message);
+            const fileStream = fs.createWriteStream(outputPath);
+            await new Promise((resolve, reject) => {
+                response.body.pipe(fileStream);
+                response.body.on('error', reject);
+                fileStream.on('finish', resolve);
+            });
+        });
+
+        console.log(`Successfully downloaded **${filename}** to **${outputPath}**`);
+
+        if (path.extname(filename) === '.zip') {
+            console.log(`Extracting **${filename}**...`);
+            const zip = new AdmZip(outputPath);
+            zip.extractAllTo(MODEL_DIR, true);
+            console.log(`Successfully extracted **${filename}** to **${MODEL_DIR}**`);
+            fs.unlinkSync(outputPath);
+            console.log(`Deleted zip file **${outputPath}**`);
         }
+
+        cache.put(cacheKey);
+    } catch (err) {
+        console.error(`Model download failed for ${repo}/${filename} after retries:`, err.message);
     }
-    console.log('TFLite download process finished.');
 }
 
 downloadModels().catch(err => {